Adaptive Metallurgical Raw Material Optimization (original) (raw)
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Modeling methods for managing raw material compositional uncertainty in alloy production
Resources, Conservation and Recycling, 2007
Operational uncertainties create disincentives for use of recycled materials in metal alloy production. One that greatly influences remelter batch optimization is variation in the raw material composition, particularly for secondary materials. Currently, to accommodate compositional variation, firms commonly set production targets well inside the window of compositional specification required for performance reasons. Window narrowing, while effective, does not make use of statistical sampling data, leading to sub-optimal usage of recycled materials. This paper explores the use of a chance-constrained optimization method, which allows explicit consideration of statistical information on composition. The framework and a case study of cast and wrought production with available scrap materials are presented. Results show that it is possible to increase the use of recycled material without compromising the likelihood of batch errors, when using this method compared to conventional window narrowing. This benefit of the chance-constrained method grows with increase in compositional uncertainty and is driven by scrap portfolio diversification.
Operational Strategies for Increasing Secondary Materials in Metals Production Under Uncertainty
Journal of Sustainable Metallurgy, 2016
Increased use of secondary raw materials in metals production offers several benefits including reduced cost and lowered energy burden. The lower cost of secondary or scrap materials is accompanied by an increased uncertainty in elemental composition. This increased uncertainty for different scraps, if not managed well, results in increased risk that the elemental concentrations in the final products fall outside customer specifications. Previous results show that incorporating this uncertainty explicitly into batch planning can modify the potential use of scrap materials while managing risk. Chance constrained formulations provide one approach to uncertainty-aware batch planning; however typical formulations assume normal distributions to represent the compositional uncertainty of the materials. Compositional variation in scrap materials has been shown to have a skewed distribution and, therefore, the performance of these models, in terms of their ability to provide effective planning, may then be heavily influenced by the structure of the compositional data used. To address this issue, this work developed several approximations for skewed distributional forms within chance constrained formulations. We explored a lognormal approximation based on Fenton's method; a convex approximation based on Bernstein inequalities; and a linear approximation using fuzzy set theory. Each of these methods was formulated 2 and case studies executed using compositional data from an aluminum remelter. Results indicate that the relationship between the underlying structure/distribution of the compositional data and how these distributions are formulated in batch planning can modify the use of secondary raw materials.
An Approach for Adaptive Parameter Setting in Manufacturing Processes
Proceedings of the 7th International Conference on Data Science, Technology and Applications, 2018
In traditional manufacturing processes the selection of appropriate process parameters can be a difficult task which relies on rule-based schemes, expertise and domain knowledge of highly skilled workers. Usually the parameter settings remain the same for one production lot, if an acceptable quality is reached. However, each part processed has its own history and slightly different properties. Individual parameter settings for each part can further increase the quality and reduce scrap. Machine learning methods offer the opportunity to generate models based on experimental data, which predict optimal parameters depending on the state of the produced part and its manufacturing conditions. In this paper, we present an approach for selecting variables, building and evaluating models for adaptive parameter settings in manufacturing processes and the application to a real-world use case.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/process-analysis-and-reduction-of-aluminium-al-and-galvanized-iron-gi-scrap-levels-by-optimizing-its-consumption-in-bus-body-building-process-using-linear-programming-model https://www.ijert.org/research/process-analysis-and-reduction-of-aluminium-al-and-galvanized-iron-gi-scrap-levels-by-optimizing-its-consumption-in-bus-body-building-process-using-linear-programming-model-IJERTV3IS061237.pdf The project was carried out at a bus body building company having three bus models (low-end, medium end & High-end buses). The company was having problems with material consumption of two materials namely Al and GI in there process i.e. the cutting section was generating scrap in large volumes of the two materials. Hence the main aim of this project was to perform scrap analysis of Aluminium (Al) and Galvanised Iron (GI), the two main materials consumed during panelling process of bus body building and come up with an alternative to reduce the scarp levels. Low end model bus was considered for this project and it accounted for 30 buses a year. The scrap generated from the existing system of two materials consumed for the low end model put together amounted to nearly 30% of the overall scrap due to factors like poor cutting allocations (materials procured come in standard sizes i.e. for Al comes in Sheets of standard size and for GI comes in rolls of standard length) or due to improper cutting methods/techniques employed by the workers working at this unit. After going through the existing system thoroughly we decided to apply one dimensional cutting stock problem a branch of Linear Programming Problem (LPP) for optimizing the current process. This approach helped us to reduce the scrap levels to 7.542% from the existing level.
Future Optimization Solution for Managing Scraps in Medium & Large Scale Manufacturing Industries
It is unlucky, but many medium and large scale manufacturing industries in South India don't take improvement of the financial prospect that scrap material from their manufacturing process presents. They think of this material as a " crucial sin " of their business without accepting the benefits and protection that a proficient scrap management program can provide. Usually, scraps can create a great deal of further revenue for their industry. In addition, Manufacturers won`t gives attention to scraps because they don`t tolerably track and control the scrap they are selling. This study focuses on to give future optimization solution that helps in managing scraps effectively and with profitability in medium and large scale manufacturing industries.
Design for Recycling in a Critical Raw Materials Perspective
Recycling, 2019
The European Union (EU) identified a number of raw materials that are strategic for its economy but suffer at the same time from a high supply risk. Such critical raw materials (CRMs) are used in a wide range of commercial and governmental applications: green technology, telecommunications, space exploration, aerial imaging, aviation, medical devices, micro-electronics, transportation, defense, and other high-technology products and services. As a result, the industry, the environment, and our quality and modern way of life are reliant on the access and use of them. In this scenario, recycling may be a strategic mitigating action aimed at reducing the critical raw materials supply risks. In this work, a design strategy is proposed for alloys selection that minimizes the number of CRMs with the lowest end-of-life recycling input rate. The method is illustrated with an example.
Sustainability of Solid Waste Recycling Processes-Case Study: Scrap Tire Recycling
Our planet has limited resources, and due to our increasing demands on a variety of products, we rely on the availability of primary and secondary resources. This paper will give an overview on the required information received from processing secondary resources. It is possible to assess the quality of the generated material fl ows with this information. By describing the material characteristics and the material fl ow uncertainties, a forecast of the material's future potential to replace primary resources may be possible. Future prospects of the quality of secondary resources, including their input and output properties may be helpful to assess their potential to substitute primary resource for example. It is the contribution of the paper to point out the necessity of knowing the whole life cycle of a product to gain the best available end-of-life option. The case study of scrap tire recycling gives an example of assessing the material's properties. Modeling recycling processes offers the potential of identifying the processing steps with regard to the main material fl ows and emissions to reduce the environmental impact and improve the economics. Material fl ow assessment and life cycle assessment can support the determination of the future potential of waste streams entering the recycling process. Some material fl ows are appropriate to replace primary resources without loss of quality. But other materials are only useful for products with minor quality. Some materials are made to never separate by itself, and therefore pure material fl ows are impossible to achieve. A model that considers different material properties of material fl ows helps to evaluate the global recycling potential. Therefore, material qualities have to be defi ned to make an assessment of sustainable management of secondary resources possible. A concept of developing a model that addresses this issue is presented in this paper. The aim of the model is to predict secondary material fl ows that are of equal quality of primary material fl ows. These material fl ows are then suitable to substitute primary resources which results in global savings in resources, both material and energy.
IFAC-PapersOnLine, 2018
The scheduling of steelmaking and continuous casting process is an important exercise for the steel industry. Uncertain processing time can significantly affect the schedule performance. To tackle this uncertainty, a multistage adaptive optimization method is proposed in this work. The proposed method can effectively handle a large number of uncertain parameters and generate an adjustable scheduling policy for online use. The method is flexible in choosing a robust objective or an expectation objective. Results demonstrate that the stochastic adaptive solution is less conservative and can be employed for optimal scheduling of the steelmaking process.